UMR CNRS 7253

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en:research [2014/06/06 15:26] – sdestercen:research [2015/05/22 09:37] – sdesterc
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 ====== Practical uncertainty representations ====== ====== Practical uncertainty representations ======
  
-Work (mainly) benefiting from collaborations and discussions with D. Dubois, M. Troffaes, E. Miranda, L. Utkin, E. Chojancki and E. Quaeghebeur+Work (mainly) benefiting from collaborations and discussions with D. Dubois, M. Troffaes, E. Miranda, L. Utkin, E. Chojnacki, E. Quaeghebeur and I. Sanchez
  
 There exist many practical representations in imprecise probability theories, including possibility distributions, belief functions, imprecise probability assignments, pari-mutuel models, imprecise cumulative distributions (p-boxes), clouds, ... There exist many practical representations in imprecise probability theories, including possibility distributions, belief functions, imprecise probability assignments, pari-mutuel models, imprecise cumulative distributions (p-boxes), clouds, ...
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 ====== Uncertainty propagation and (in)dependence modelling ====== ====== Uncertainty propagation and (in)dependence modelling ======
  
-Work (mainly) benefiting from collaborations and discussions with D. Dubois, G. De Cooman, E. Chojnacki, J. Baccou, T. Burger, M. Sallak, M.C.M. Troffaes, F. Coolen, S. Ferson and F. Aguirre+Work (mainly) benefiting from collaborations and discussions with D. Dubois, G. De Cooman, E. Chojnacki, J. Baccou, T. Burger, M. Sallak, M.C.M. Troffaes, F. Coolen, S. Ferson, F. Aguirre and I. Sanchez
  
 How to propagate uncertainty analysis in various models is an important issue that may face several difficulties. Most of my research in this domain has concerned the propagation of uncertainty model through deterministic functions with methods combining Monte-Carlo simulation and interval analysis, with an industrial risk-assessment purpose.  How to propagate uncertainty analysis in various models is an important issue that may face several difficulties. Most of my research in this domain has concerned the propagation of uncertainty model through deterministic functions with methods combining Monte-Carlo simulation and interval analysis, with an industrial risk-assessment purpose. 
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 ====== Learning problems ====== ====== Learning problems ======
  
-Work (mainly) benefiting from collaborations and discussions with B. Quost, T. Denoeux, B. Ben Yaghlane, N. Sutton-Charani, G. Yang, M. Masson, E. Hüllermeier, A. Antonucci and G. Corani+Work (mainly) benefiting from collaborations and discussions with B. Quost, T. Denoeux, B. Ben Yaghlane, N. Sutton-Charani, G. Yang, M. Masson, E. Hüllermeier, A. Antonucci, G. Corani, M. Poss and N. Ben Abdallah
  
 Outside of extending some classical classifiers (k-NN methods, Naïve networks) to imprecise probabilistic settings, our work currently focuses on the combination of classifiers, to address both the usual multi-classification problem, as well as more complex problems such as label ranking and multilabel classification. Outside of extending some classical classifiers (k-NN methods, Naïve networks) to imprecise probabilistic settings, our work currently focuses on the combination of classifiers, to address both the usual multi-classification problem, as well as more complex problems such as label ranking and multilabel classification.
  
-One of our current favorite field of investigation is the so-called binary decomposition, where complex problems are decomposed in several binary ones (facilitating the learning but increasing the number of models to learn).  +One of our current favorite field of investigation is the so-called binary decomposition, where complex problems are decomposed in several binary ones (facilitating the learning but increasing the number of models to learn). In the future, we plan to focus more on active learning topics, where imprecise probabilistic methods can have an important role to play, due to their ability to identify cases where information is missing. 
  
  
 ====== Applications ====== ====== Applications ======
  
-Work (mainly) benefiting from collaborations and discussions with P. Buche, B. Charnomordic, O. Strauss, V. Guillard, E. Chojnacki, M. Sallak+Work (mainly) benefiting from collaborations and discussions with P. Buche, B. Charnomordic, O. Strauss, V. Guillard, E. Chojnacki, M. Sallak, I. Thouvenin
  
 We have applied ideas coming from imprecise probability theories and more generally concerning uncertainty handling to a number of frameworks, including: We have applied ideas coming from imprecise probability theories and more generally concerning uncertainty handling to a number of frameworks, including:
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   * Risk analysis and robust design (E. Chojancki, V. Guillard, M. Sallak)   * Risk analysis and robust design (E. Chojancki, V. Guillard, M. Sallak)
   * Process modelling (C. Baudrit)   * Process modelling (C. Baudrit)
 +  * Virtual training (I. Thouvenin)

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